Our experiments show that symmetry has a wider application,
beyond just spatial translation within images.
In particular, we apply this approach to
learning the identity function on binary digits
[1]} and to
rule learning on sequences of words
[2]}.
Successful performance on these tasks
requires generalisation to unseen features,
i.e. novel digits or words.
In each case, we analyse the structure of the domain
to identify a symmetry that is relevant to the task
and impose this on the network in terms of
a convolutional architecture.
Concretely, this results in weight sharing between the
seen and unseen features,
i.e. across digit positions or syllables,
allowing effective generalisation to the test instances.
